MétaCan
Menu
Back to cohort
Record W2112102997 · doi:10.5539/jsd.v6n1p90

Payments for Watershed Protection Services: Emerging Lessons from the Philippines

2012· article· en· W2112102997 on OpenAlexvenueno aff
Daniel Gaitán Cremaschi, Rodel D. Lasco, Rafaela Jane Delfino

Bibliographic record

VenueJournal of Sustainable Development · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesWatershedPayment for ecosystem servicesPaymentWatershed managementBusinessLivelihoodIncentiveGovernment (linguistics)Environmental resource managementDeforestation (computer science)Environmental planningAgricultureEconomicsGeographyEcosystemEcologyFinance

Abstract

fetched live from OpenAlex

There is growing interest on payments for ecosystem services (PES) in developing countries including the Philippines. Watersheds have been degraded through deforestation and subsequent conversion to other land cover, principally for agriculture. In the last decade, several Payments for Watershed Services schemes have been implemented and this paper is an attempt to assess the form of incentives or rewards that have been provided to upland communities in a number of sites under different management leadership in the Philippines. We reviewed four cases specifically related to watershed services in the: 1) Bakun Watershed, 2) Maasin Watershed, 3) Sibuyan Watershed, and 4) Baticulan Watershed. The case studies of varying stages of implementation has shown that the chances of success of PES schemes in promoting watershed conservation and rehabilitation as well as in improving the livelihoods of upland communities is constrained by incomplete information and knowledge about the interaction between ecosystem properties and provision of services, and the difficulty in establishing voluntary participation and conditionality of payments. In this paper, we argued that institutions may enable or hinder the successful implementation of PES. The role of the local government as intermediaries is crucial in the process of establishing PES more particularly in the information dissemination and education of the key stakeholders. The case studies also showed how PES programs are reinforced by the presence of non-government organizations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.225
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207